A Hybrid Heuristic Algorithm for the Intelligent Transportation Scheduling Problem of the BRT System

2015 ◽  
Vol 24 (4) ◽  
pp. 437-448 ◽  
Author(s):  
Zheng Ning ◽  
Chen Tao ◽  
Lin Fei ◽  
Xu Haitao
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Heuristic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Selection Probability ◽  
Hybrid Heuristic ◽  
Hybrid Heuristic Algorithm ◽  
Annealing Algorithm

AbstractThis work proposes a hybrid heuristic algorithm to solve the bus rapid transit (BRT) intelligent scheduling problem, which is a combination of the genetic algorithm, simulated annealing algorithm, and fitness scaling method. The simulated annealing algorithm can increase the local search ability of the genetic algorithm, so as to accelerate its convergence speed. Fitness scaling can reduce the differences between individuals in the early stage of the algorithm, to prevent the genetic algorithm from falling into a local optimum through increasing the diversity of the population. It can also increase the selection probability of outstanding individuals, and speed up the convergence at the late stage of the algorithm, by increasing the differences between individuals. Using real operational data of BRT Line 1 in a city of Zhejiang province, the new scheduling scheme can be obtained through algorithm simulation. The passengers’ total waiting time in a single way will be reduced by 40 h on average under the same operating cost compared with the original schedule scheme in a day.

scholarly journals Computing a Complex Network Hierarchical Structure for Financial Market Networks on the Basis of the Hybrid Heuristic Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jiannan Yu ◽  
Jinlou Zhao
Keyword(s):  
Risk Management ◽  
Simulated Annealing ◽  
Hierarchical Structure ◽  
Heuristic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Empirical Studies ◽  
Hybrid Heuristic ◽  
Interbank Markets ◽  
Hybrid Heuristic Algorithm ◽  
Annealing Algorithm

The recent empirical studies showed that money center networks in interbank markets are more robust and stable. Therefore, the research on layered financial networks is a key part of the systemic risk management. Various methods have been proposed in prior studies to find optimal partitioning of interbank networks into core and periphery subsets. However, these methods that have been adopted with approximation methods, in general, do not guarantee optimal bipartition. In this paper, a genetic simulated annealing algorithm is presented to detect a hierarchical structure in interbank networks as a hybrid heuristic algorithm, while its effects are also analyzed. The optimization of the error score for the core-periphery model is mathematically developed firstly as an improved expression of the optimization function, which incorporates the genetic algorithm into a simulated annealing algorithm to guarantee the optimal bipartition and to jump from a local optimization. The results of this algorithm are finally verified by empirical analysis of interbank networks; and, through the immunity strategy under the risk diffusion model, the significance of core-periphery structure to risk management is verified.

scholarly journals A Hybrid Heuristic Algorithm for Ship Block Construction Space Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Shicheng Hu ◽  
Tengjiao Liu ◽  
Song Wang ◽  
Yonggui Kao ◽  
Xuedong Sun
Keyword(s):  
Heuristic Algorithm ◽  
Production Scheduling ◽  
Initial Solution ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Hybrid Heuristic ◽  
Hybrid Heuristic Algorithm ◽  
Block Construction ◽  
Sorting Process ◽  
Bottom Left

Ship block construction space is an important bottleneck resource in the process of shipbuilding, so the production scheduling optimization is a key technology to improve the efficiency of shipbuilding. With respect to ship block construction space scheduling problem, a hybrid heuristic algorithm is proposed in this paper. Firstly, Bottom-Left-Fill (BLF) process is introduced. Next, an initial solution is obtained by guiding the sorting process with corners. Then on the basis of the initial solution, the simulated annealing arithmetic (SA) is used to improve the solution by offering a possibility to accept worse neighbor solutions in order to escape from local optimum. Finally, the simulation experiments are conducted to verify the effectiveness of the algorithm.

Research on Image Technology with Application of K-Means Based on Genetic Simulated Annealing Algorithm in CT Image Segmentation

2014 ◽  
Vol 1022 ◽  
pp. 269-272
Author(s):  
Ling Li Zhu ◽  
Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Simulated Annealing ◽  
Clustering Analysis ◽  
Simulated Annealing Algorithm ◽  
Local Optimum ◽  
Initial Condition ◽  
Ct Image ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Aiming at the characteristic of medical images, this paper presents the improved genetic simulated annealing algorithm with K-means clustering analysis and applies in medical CT image segmentation. This improved genetic simulated annealing algorithm can be used to globally optimize k-means image segmentation functions to solve the locality and the sensitiveness of the initial condition. It can automatically adjust the parameters of genetic algorithm according to the fitness values of individuals and the decentralizing degree of individuals of the population and keep the variety of population for rapidly converging, and it can effectively avoid appearing precocity and plunging into local optimum. The example shows that the method is feasible, and better segmentation results have got to satisfy the request for 3D reconstruction, compared with k-means image segmentation and genetic algorithm based image segmentation.

Artificial Intelligence Application in Modern Management

2010 ◽  
Vol 37-38 ◽  
pp. 203-206
Author(s):  
Rong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Local Optimum ◽  
Efficient Operation ◽  
Modern Management ◽  
Global Optimal ◽  
Annealing Algorithm

Modern management is a science of technology that adopts analysis, test and quantification methods to make a comprehensive arrangement of the limited resources to realize an efficient operation of a practical system. Simulated annealing algorithm has become one of the important tools for solving complex optimization problems, because of its intelligence, widely used and global search ability. Genetic algorithm may prevent effectively searching process from restraining in local optimum, thus it is more possible to obtains the global optimal solution.This paper solves unconstrained programming by simulated annealing algorithm and calculates constrained nonlinear programming by genetic algorithm in modern management. So that optimization process was simplified and the global optimal solution is ensured reliably.

scholarly journals Optimization of Healthy Diet Menu Variation using PSO-SA

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Imam Cholissodin ◽  
Ratih Kartika Dewi
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimal Parameter ◽  
Pso Algorithm ◽  
Healthy Diet ◽  
Local Optimum ◽  
The Family ◽  
Lower Complexity ◽  
Annealing Algorithm

Abstract. Optimal healthy diet in accordance with the allocation of cost needed so that the level of nutritional adequacy of the family is maintained. The problem of optimal healthy diet (based on family budget) can be solved with genetic algorithm. The algorithm particle swarm optimization (PSO) has the same effectiveness with genetic algorithm but PSO is superior in terms of efficiency, PSO algorithm has a lower complexity than genetic algorithm. However, genetic algorithms and PSO have a problem of local optimum because these algorithm associated with random numbers. To overcome this problem, PSO algorithm will be improved by combining it with simulated annealing algorithm (SA). Simulated annealing algorithm is a numerical optimization algorithms that can avoid local optimal. From our results, optimal parameter for PSO-SA are popsize 280, crossover rate 0.6, mutation rate 0.4, first temperature 1, last temperature 0.2, alpha 0.9, and generation size 100.Keywords: PSO, SA, optimization, variation, healthy diet menu.

scholarly journals A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

2021 ◽  
Author(s):  
Chin-Chia Wu ◽  
Ameni Azzouz ◽  
Jia-Yang Chen ◽  
Jianyou Xu ◽  
Wei-Lun Shen ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Completion Time ◽  
Simulated Annealing Algorithm ◽  
Optimal Schedule ◽  
Computational Studies ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Large Size ◽  
Annealing Algorithm ◽  
One Machine

AbstractThis paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

scholarly journals Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 219
Author(s):  
Xiang Tian ◽  
Xiyu Liu
Keyword(s):  
Local Search ◽  
Heuristic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Membrane System ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Hybrid Heuristic ◽  
Shop Scheduling ◽  
Hybrid Heuristic Algorithm

In real industrial engineering, job shop scheduling problem (JSSP) is considered to be one of the most difficult and tricky non-deterministic polynomial-time (NP)-hard problems. This study proposes a new hybrid heuristic algorithm for solving JSSP inspired by the tissue-like membrane system. The framework of the proposed algorithm incorporates improved genetic algorithms (GA), modified rumor particle swarm optimization (PSO), and fine-grained local search methods (LSM). To effectively alleviate the premature convergence of GA, the improved GA uses adaptive crossover and mutation probabilities. Taking into account the improvement of the diversity of the population, the rumor PSO is discretized to interactively optimize the population. In addition, a local search operator incorporating critical path recognition is designed to enhance the local search ability of the population. Experiment with 24 benchmark instances show that the proposed algorithm outperforms other latest comparative algorithms, and hybrid optimization strategies that complement each other in performance can better break through the original limitations of the single meta-heuristic method.

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